Research on fault component extraction and fault type identification of rotating machinery based on MDSM and a novel convolutional neural network

Abstract Given the complexity and difficulty in extracting and recognizing multi-axis mechanical fault components, a method for fault extraction and identification based on the multi-Axis displacement superposition method (MDSM) and a novel convolutional neural network (NCNN) is proposed. In the proposed MDSM method, first, correlation analysis is used to determine the operational status of the mechanical system and to identify the location of faults in the multi-axis rotating mechanical system. Secondly, a simplified initial point selection process is introduced to segment the collected fault component. Subsequently, a signal superposition method with position offset correction is employed to perform position correction and superposition operations on the segmented signals, enhancing the accuracy of the fault signal. Finally, the front end of the superimposed signals is extracted as the fault component, completing the separation and extraction of the fault components. For the extracted fault signals, an NCNN is designed for fault-type identification. NCNN improves computational efficiency and effectively completes fault feature identification through a lightweight network architecture and a nonlinear learning rate scheduling strategy. The results of the experiment show that the proposed method can accurately determine the fault occurrence location, extract the fault components, and achieve high-accuracy fault type identification.